import numpy as np
from scipy import special
import h5py

import os, sys

# Rijndael S-box
sbox =  np.array(
        [0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67,
        0x2b, 0xfe, 0xd7, 0xab, 0x76, 0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59,
        0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0, 0xb7,
        0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1,
        0x71, 0xd8, 0x31, 0x15, 0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05,
        0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75, 0x09, 0x83,
        0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29,
        0xe3, 0x2f, 0x84, 0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b,
        0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf, 0xd0, 0xef, 0xaa,
        0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c,
        0x9f, 0xa8, 0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc,
        0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2, 0xcd, 0x0c, 0x13, 0xec,
        0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19,
        0x73, 0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee,
        0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb, 0xe0, 0x32, 0x3a, 0x0a, 0x49,
        0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79,
        0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4,
        0xea, 0x65, 0x7a, 0xae, 0x08, 0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6,
        0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a, 0x70,
        0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9,
        0x86, 0xc1, 0x1d, 0x9e, 0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e,
        0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf, 0x8c, 0xa1,
        0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0,
        0x54, 0xbb, 0x16])


def shuffle_all(predictions, plaintexts, keys):
    rng_state = np.random.get_state()
    np.random.shuffle(predictions)
    np.random.set_state(rng_state)
    np.random.shuffle(plaintexts)
    np.random.set_state(rng_state)
    np.random.shuffle(keys)

    return predictions, plaintexts, keys


def get_log_prob(predictions, plaintext):
    predictions = np.squeeze(predictions)
    n_classes = predictions.shape[0]
    keys = np.arange(n_classes, dtype=int)
    x_xor_k = np.bitwise_xor(keys, plaintext)
    z = np.take(sbox, x_xor_k)
    log_prob = np.take(predictions, z)

    return log_prob


def compute_key_rank(predictions, plaintexts, keys):
    n_samples, n_classes = predictions.shape
    plaintexts = plaintexts[:n_samples]
    keys = keys[:n_samples]
    predictions, plaintexts, keys = shuffle_all(predictions, plaintexts, keys)

    predictions = special.softmax(predictions, axis=1)
    predictions = np.log(predictions+1e-40)

    cum_log_prob = np.zeros((n_samples, n_classes))
    last_log_prob = np.zeros((1, n_classes))
    for i in range(n_samples):
        log_prob = get_log_prob(predictions[i, :], plaintexts[i])
        last_log_prob += log_prob
        cum_log_prob[i, :] = last_log_prob

    sorted_keys = np.argsort(-cum_log_prob, axis=1)
    key_ranks = np.zeros((n_samples), dtype=int) - 1
    for i in range(n_samples):
        for j in range(n_classes):
            if sorted_keys[i, j] == keys[i]:
                key_ranks[i] = j
                break

    for i in range(n_samples):
        assert key_ranks[i] >= 0, "Assertion failed at index %s" % i
        
    return key_ranks


if __name__ == '__main__':
    data_path = sys.argv[1]

    data = h5py.File(data_path, 'r')

    for i in range(10):
        label = data['Profiling_traces']['labels'][i]
        ptest = data['Profiling_traces']['metadata'][i]['plaintext'][2]
        key = data['Profiling_traces']['metadata'][i]['key'][2]

        print(str(label)+'/'+str(sbox[np.bitwise_xor(ptest, key)])+'/'+str(key))

